Abstract
Constrained clustering uses pairwise constraints, i.e., pairs of data that belong to the same or different clusters, to indicate the user-desired contents. In this paper, we propose a new constrained clustering algorithm, which can utilize both must-link and cannot-link constraints. It first adaptively determines the influence range of each constrained data, and then performs clustering on the expanded range of data. The promising experiments on the real-world data sets demonstrate the effectiveness of our method.
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References
Basu, S., Davidson, I., Wagstaff, K.L.: Constrained Clustering. In: Advances in Algorithms, Theory, and Applications. Chapman and Hall/CRC (2008)
Li, Z., Liu, J., Tang, X.: Constrained clustering via spectral regularization. In: Proceeding of Computer Vision and Pattern Recognition, pp. 421–428 (2009)
Wagsta, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained k-means clustering with background knowledge. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 577–584 (2001)
Klein, D., Kamvar, S., Manning, C.: From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In: Proceedings of the Nineteenth International Conference on Machine Learning, pp. 307–314 (2002)
Shental, N., Bar-hillel, A., Hertz, T., Weinshall, D.: Computing gaussian mixture models with em using equivalence constraints. In: Advances in Neural Information Processing Systems 16, pp. 465–472 (2003)
Yu, S., Shi, J.: Segmentation given partial grouping constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2), 173–180 (2004)
De Bie, T., Suykens, J., De Moor, B.: Learning from general label constraints. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 671–679. Springer, Heidelberg (2004)
Kamvar, S., Klein, D., Manning, C.: Spectral learning. In: International Joint Conference on Artificial Intelligence, pp. 561–566 (2003)
Kulis, B., Basu, S., Dhillon, I., Mooney, R.: Semi-supervised graph clustering: A kernel approach. Machine Learning 74, 1–22 (2009)
Liu, W., Ma, S., Tao, D., Liu, J., Liu, P.: Semi-supervised sparse metric learning using alternating linearization optimization. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1139–1148 (2010)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: The 20th International Conference on Machine Learning (ICML), pp. 912–919 (2003)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 6, 721–741 (2000)
Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems 14, pp. 849–856 (2001)
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He, P., Xu, X., Zhang, L., Zhang, W., Li, K., Qian, H. (2014). Constrained Community Clustering. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_87
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DOI: https://doi.org/10.1007/978-3-319-09333-8_87
Publisher Name: Springer, Cham
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